There’s much talk about AI (Artificial Intelligence) and ML (Machine Learning). But how much is just talk and how much is a reality? Giants such as IBM, Microsoft, Dell EMC, IDC, the Harvard Business Review, and many others have weighed in on the subject. If we are to draw one consensus from all of these, it is that AI and ML are coming of age.

In some cases, AI and ML are already in place, notably in intelligent agents such as Cortana and Alexa. Their “brains” are indisputably far superior to ours. They have encyclopedic knowledge and can answer tens of thousands of questions voiced in natural language.

We also know that companies like Amazon have successfully implemented AI and ML into their customer-facing applications, guiding consumers through the buying process and suggesting future purchases. If you shop on Amazon, you know what I mean. But how does that translate into advances for manufacturing, or distribution, or financial services, or healthcare?

Research vs. Reality

Another consensus among AI leaders is the need to take decades of research out of the theoretical realm and achieve tangible results. Investments in AI are escalating into billions of dollars. But companies need to see a return on those investments if they are going to continue to spend that kind of money on AI projects.

How much money can be saved if AI can issue maintenance alerts before a machine breaks down? Or better yet, how much money can be saved if intelligent machines could self-repair? There are in fact solutions with built-in AI operating today to self-manage data centers, automatically allocating resources to workloads and load balancing, resulting on quantifiable savings for enterprise companies, in any industry, that have been struggling to contain costs.

Data is Key

This is yet another area of consensus. AI and ML applications and operations are all predicated on data. But not just any data. It must be clean data. And that is not simple. As data proliferates it becomes more and more difficult to separate the wheat from the chaff, so to speak. With petabytes of data coming across a network, the useful and accurate data must be identified before it can be analyzed and put to use in predictive analytics.

The algorithms for predictive analytics may return potentially expensive errors if the underlying data is faulty. But with clean data, algorithms can be and are being used successfully in many industries for predictive modeling that drives growth and value.

AI today has the ability to look “at multiple datasets in context, using machine learning algorithms to extract not only insights but predictions, recommendations, and actions based on those findings. Most importantly, intelligence systems use the outcomes of these actions to (re)train their algorithms.”1

Replacing Humans or Driving Job Growth

The jury is still out on this question which is rather important to those of us who hope to keep our jobs. Some renowned AI leaders believe that machines will indeed replace humans, driving job losses. Other equally qualified experts predict that the corporate growth spurred by AI and ML will naturally drive job growth. It’s a comforting thought, but only time will tell.